MLP-TLBO: Combining Multi-Layer Perceptron Neural Network and Teaching-Learning-Based Optimization for Breast Cancer Detection

Symptoms and types of breast cancer, like most other diseases, vary from person to person. This is while some infected people may not have any symptoms. Breast cancer is one of the common cancers among women, of course, some types of it also affect men. Basically, timely and early diagnosis before t...

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Vydané v:Cybernetics and systems Ročník 56; číslo 3; s. 242 - 269
Hlavní autori: Cui, Yuzhen, Li, Xiaoqian, Wang, Yaoyao, Yuan, Weiqiang, Cheng, Xianwu, Samiei, Moslem
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Taylor & Francis 03.04.2025
Predmet:
Breast cancer
evolutionary approach
MLP
TLBO
tuning parameters
Wisconsin database
ISSN:0196-9722, 1087-6553
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Popis
Shrnutí:Symptoms and types of breast cancer, like most other diseases, vary from person to person. This is while some infected people may not have any symptoms. Breast cancer is one of the common cancers among women, of course, some types of it also affect men. Basically, timely and early diagnosis before the progress of the disease can significantly provide treatment grounds. There are many approaches to breast cancer diagnosis. Classification of patients in terms of benign or malignant cancer using data mining techniques has attracted the attention of many researchers. Data mining can provide a medical assistant system with very low cost and high accuracy for early detection of breast cancer. In this regard, this study proposes a data mining-based method by a combining Multi-Layer Perceptron (MLP) neural network and an evolutionary approach for breast cancer diagnosis. We aim to tune MLP parameters using an evolutionary approach. Here, Teaching-Learning-Based Optimization (TLBO) is used as a new evolutionary approach. However, the capabilities of approaches such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Open-Source Development Model Algorithm (ODMA) are analyzed compared to TLBO. The proposed method named MLP-TLBO can simultaneously adjust all MLP parameters (i.e., effective features and their number, number of hidden layers, number of neurons in each hidden layer, and weights of links). To improve the classification process, MLP-TLBO develops an ensemble classification mechanism in which multiple MLPs simultaneously model the training data. The evaluation of the proposed method is done on the Wisconsin Breast Cancer Database. We use three common datasets from Wisconsin including WBCD, WDBC and WPBC for simulations. Based on the results, the proposed method has promising results and averages between 7% and 26% better performance compared to similar methods.
ISSN:0196-9722
1087-6553
DOI:10.1080/01969722.2022.2159151